Extracting Entities of Interest from Comparative Product Reviews

被引：9

作者：

Arora, Jatin ^{[1
]}

Agrawal, Sumit ^{[1
]}

Goyal, Pawan ^{[1
]}

Pathak, Sayan ^{[2
]}

机构：

[1] Indian Inst Technol, Kharagpur, W Bengal, India

[2] Microsoft Res, Redmond, WA USA

来源：

CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT | 2017年

关键词：

Comparison Mining; Deep Learning; Opinion Extraction;

D O I：

10.1145/3132847.3133141

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites. Any comparative product review has three major entities of information: the names of the products being compared, the user opinion (predicate) and the feature or aspect under comparison. All these informing entities are dependent on each other and bound by the rules of the language, in the review. We observe that their inter-dependencies can be captured well using LSTMs. We evaluate our system on existing manually labeled datasets and observe out-performance over the existing Semantic Role Labeling (SRL) framework popular for this task.

引用

页码：1975 / 1978

页数：4

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